The widespread adoption of chest CT for lung cancer screening will greatly increase the workload of chest radiologists. Contributing to this effort is the need for radiologists to differentiate between localized nodules and slices through linear structures such as blood vessels, in each of a large number of slices acquired for each subject. To increase efficiency and accuracy, thin slices can be combined to provide thicker slabs for presentation, but the resulting superposition of tissues can make it more difficult to detect and characterize smaller nodules. The stereo display of a stack of thin CT slices may be able to clarify three-dimensional structures, while avoiding the loss of resolution and ambiguities due to tissue superposition.
The current work focuses on the development and evaluation of stereo projection models that are appropriate for chest CT. As slices are combined into a three dimensional structure, maximum image intensity, which is limited by the display, must be preserved. But, compositing methods that effectively average slices together typically reduce contrast of subtle nodules. For monoscopic viewing, orthographic maximum-intensity projection (MIP), of thick slabs, has been employed to overcome this effect, but this method provides no information of depth or of the geometrical relationships between structures. Our comparison of various rendering options indicates that a stereographic perspective transformation, used in conjunction with a compositing model that combines maximum-intensity projection with an appropriate brightness weighting function, shows promise for this application. The main drawback uncovered was that, for the images used in this study, the lung volume was undersampled in the z-direction, resulting in certain unavoidable image artifacts.